1. Field of the Invention
The present invention relates to neurophysiological monitoring systems that provide clinical and(or) research data on the relationship(s) between changes taking place in the central nervous system during stress, surgery, anesthesia, and other conditions resulting in dynamic brain state changes; and resulting changes seen in the autonomic nervous system and the cardiovascular system. Such systems, when noninvasive, provide less risk to subjects while guiding management, diagnosis, and treatment and(or) alerting observers to changes like awareness under anesthesia, brain ischemia, pain, and severe stress.
Since neurophysiological monitoring equipment per se adds to the complexity of setup, increases the demands on the operator, raises the cost of care, and if used improperly, adds to the risk of mistaken interpretation, a system that can predict brain states using already implemented cardiovascular monitoring modalities will allow for such predictive capabilities while minimizing risk, cost, and added complexity of such a setup.
2. Description of the Prior Art
Neurological and cardiovascular physiological monitoring systems currently in use utilize a number of methods to provide observers with information on the functional states of the central nervous system, autonomic nervous system, and the cardiovascular system.
Invasive cardiovascular dynamic measurements in use include analysis of peripheral arterial pulses, pressures taken from catheters placed in the great veins, the heart chambers, the pulmonary arterial bed, and the pulmonary venous bed; continuous and intermittent thermodilutional methods of cardiac output assessment; radiotracer scanning; and continuous fiberoptic oximetric assessment of central mixed venous hemoglobin oxygen saturation.
Noninvasive cardiovascular and neurological monitoring systems employ electrophysiological measurements from the skin based on native bioelectric impulses (as in standard electroencephalography (EEG), processed EEG, standard electrocardiography (ECG), and processed ECG) or resulting from programmed stimuli to the skin or sensory organs as in somatosensory evoked potentials (SSEP), brainstem audio evoked response (BAER), visual evoked potentials (VEP), motor evoked potentials (MEP), and facial electromyography (FACE); or from programmed current passed through the body to obtain an index of bioimpedance as a means of predicting cardiovascular dynamics.
Other noninvasive systems employ plethysmographic or doppler techniques to provide a pulse waveform for analysis or utilize ultrasound in the form of echocardiography imaging, simple surface or esophageal Doppler analysis, or detect sound as in phonocardiography.
Recently, a great deal of research has been conducted into mathematical models to predict the cardiac output, left ventricle filling and ejection volumes of the heart, and stroke work of the heart; based on changes in the bioelectric impedance of the thorax to varying weak alternating electrical currents. This technique, known as thoracic bioimpedance, has been shown to accurately and noninvasively predict changes in cardiovascular dynamics in response to various stimuli including hemorrhage, shock, stress, and anesthesia (U.S. Pat. No. 5,309,917). Biboulet et. al. (Br.J.Anesth. 1996;76(1):81-84) for example, using the technique of thoracic bioimpedance, has shown that patients exposed to blood dilution have a much different cardiovascular response when anesthetized during said dilution. This supports the concept that bioimpedance-based cardiovascular analysis is sensitive to cardiovascular changes that occur during the transition from the awake to the anesthetized state. Thus far, bioimpedance per se has not been used as a gauge to measure level of consciousness by itself nor in combination with direct cerebral monitoring as with evoked potentials or EEG. Since subtle patterns of change in cardiovascular patterns are more telling with respect to changes in brain states, and absolute numbers less important, Giuffre and Anzano have recently proposed an improvement in existing bioimpedance modeling involving the use of a pulse or doppler plethysmographic signal from the heart itself or a peripheral artery to more accurately estimate systolic ejection time in combination with traditional bioimpedance cardiovascular modeling algorithms (Giuffre, Anzano, U. S. Patent filing November, 1977).
Several authors have also recently shown that differing levels of consciousness, stress, and anesthesia result in changes in the normal heart rate variability that occurs in response to breathing and which has been termed cardiac vagal tone (Billman et. al., Heart Circ Physiol, 1990; 27:H896-H902) because the vagus nerve of the parasympathetic autonomic nervous system is thought to mediate these heart rate changes, also described by Porges (Pediatrics 1992, 90:498-504) and which forms the basis for biophysical analysis in U.S. Pat. No. 4,510,944 and in Jaffe et. al. (J Clin Monit 1994, 10(1):45-48). Anesthetic depth has been shown to affect vagal tone (Ireland et. al., Br J Anesth 1996, 76(5):657-62), (Latson et. el., J Clin Anesth 1992;4(4):265-76), (Alkire et. al., Anesthesiology 1997; 87(3A);A175). These methods utilize ECG or pulse measurements and thus far have not incorporated EEG-trained classification and prediction computer models into their data collection and processing.
Other methods that have attempted to reliably measure brain activity as a function of level-of-consciousness, anesthetic depth and(or) state of alertness include sensors measuring microexpression changes in the face (U.S. Pat. No. 5,195,531)(Struys et. al., Anesthesiology 1997; 87(3A):A9), heart rate response to ocular compression (Shapiro et. al., Psychophysiology 1996;33(1):54-62), contractile response of the lower esophagus (Maccioli et. al., J Clin Monit 1988;4(4):247-55), and the H-reflex measuring amplitude and latency of spinal reflex arc at the tibial nerve (Magladery et. al., Bull Johns Hopkins Hosp 1951;88:499) and other reflex arc responses (Chabal et. al., Anesthesiology 1989;70:226-29). Electrical stimulation has been shown to even affect levels of central nervous system chemical mediators of mood and pain sensation. Low frequency peripheral electrical stimulation raises brain levels of endogenous opiates and is antagonized by opiate antagonists (Chiang, Scientia Sinia 1973;16:210-217, and Pomerantz, Basis of Acupuncture, Springer Verlag 1991:250-260). Higher frequency peripheral electrical stimulation raises brain amines like serotonin (Han, Scientia Sinia 1979; 22:91-104 and Lichtman et. al. Behavioral Neuroscience 1991; 105(5):687-98). None of these brain responses to various modes of stimulation have been correlated with direct neurological monitoring data in combination with a trained computer classification and prediction model. A neural net was used in comparing hemodynamic responses to electroencephalography and facial myography (Watt et. al., Anesthesiology 1995;83(3A):A32, and Lang et. al., Anesthesiology 1994;81(3A):A197) but no attempt was made at using the hemodynamic data to back-predict the neurophysiological data.
Beyond simple, wave processed (Billard et. al., Anesthesiology 1993; 79(3A):A174), and bispectral index electroencephalography (U.S. Pat. No. 5,010,891), (Rosow et. al., Anesth Clin of NA: Annual of Anes Pharm 1998;2:89-107),(Billard et. al., Anesthesiology 1996; 85(3A):A32); other variations have been utilized to measure anesthetic depth and level of consciousness. In U.S. Pat. No. 4,869,264, the EEG response to infrared light passed through closed eyelids is utilized. Even further involvement of the body sensory means via stimulation are demonstrated in U.S. Pat. No. 4,570,640 where the body surface is stimulated, and in U.S. Pat. No. 4,201,224 where statistical Z transformations are used to process multimodal stimulation response as measured by EEG, ECG, and evoked potentials. Though this method uses a statistical predictive model, it does not attempt to create data prediction in the absence of neurological monitoring. In the method described by Muthuswamy et. al. (J Clin Monitoring 1996; 12:353-364), measurement of end tidal expired carbon dioxide is utilized in combination with processed EEG and a predictive computer algorithm. None however, have specifically combined their specific method of physiologic monitoring with neurologic monitor output to train a classification/prediction model for predicting states of central nervous system activity as a function of said physiological monitoring in the absence of neurologic monitor data.
Evoked potential monitoring has also been utilized as a measure of level of consciousness (Doi et. al., Br J Anes 1997; 78:180-84) as well as positron-emission tomography (PET) scanning (Anesthesiology 1996;85(3A):A9).
Various computer techniques have been employed in the creation of classification/prediction models for management of biophysical monitoring data. Neural network programming algorithms have been shown to be effective for recognizing patterns in biophysical monitoring modes (Baxt WG, Lancet 1995;346:1135-38). Kloppel describes the use of neural networks in EEG analysis (Neuropsychobiology 1994;29:33-38) along with Jando et. al. (Electroencephalography and Clin Neurophys 1993;86:100-109). Neural networks in EEG analysis have even been used in analyzing stages of sleep (Schaltenbrand et. al., Sleep 1996; 19(1):26-35). Neural networks have also been utilized in pattern recognition of neurological evoked potential signals (Laskaris et. al., Electroencephalography and Clin Neurophys 1997;104:151-56).
Statistical methods have also been used as previously mentioned in the citation of U.S. Pat. No. 4,570,640 and in other modes of monitoring including neonatal monitoring of heart rate variability and audiology testing (both cited by Abtech Corp., Charlottesville, Va., 1995).
Newer software methods combine statistical analysis with neural net training (e.g. Model Quest software, Abtech Corp., Charlottesville, Va., 1996) or neural net training with genetic algorithms for inducing changes in neural net configuration to auto-optimize the model during training (e.g. Neuroshell Easy Predictor; Neuroshell Easy Classifier; both by Ward Systems Group, Frederick, Md., 1997).
None of the above methods combine specific methods of physiological assessment with EEG-based training of neural networks to allow prediction and assessment of either state of anesthetic depth or state of consciousness by using said EEG-trained neural net to predict the state of the central nervous system as a function of the output of said physiological assessment and without the input from an EEG or other brain-based monitoring system.